Objectives: In the present study, we developed new software for quantitative analysis of cervical vertebrae maturation, and we evaluated its applicability through a multinomial logistic regression model (MLRM).
Methods: Digitized images of the bodies of the second (C2), third (C3) and fourth (C4) cervical vertebrae were analysed in cephalometric radiographs of 236 subjects (116 boys and 120 girls) by using a software developed for digitized vertebrae analysis. The sample was initially distributed into 11 categories according to the Fishman's skeletal maturity indicators and were then grouped into four stages for quantitative cervical maturational changes (QCMC) analysis (QCMC I, II, III and IV). Seven variables of interest were measured and analysed to identify morphologic alterations of the vertebral bodies in each QCMC category.
Results: Statistically significant differences (p < 0.05) were observed among all QCMC categories for the variables analysed. The MLRM used to calculate the probability that an individual belonged to each of the four cervical vertebrae maturation categories was constructed by taking into account gender, chronological age and four variables determined by digitized vertebrae analysis (Ang_C3, MP_C3, MP_C4 and SP_C4). The MLRM presented a predictability of 81.4%. The weighted κ test showed almost perfect agreement (κ = 0.832) between the categories defined initially by the method of Fishman and those allocated by the MLRM.
Conclusions: Significant alterations in the morphologies of the C2, C3 and C4 vertebral bodies that were analysed through the digitized vertebrae analysis software occur during the different stages of skeletal maturation. The model that combines the four parameters measured on the vertebral bodies, the age and the gender showed an excellent prediction.
Keywords: cervical vertebrae; logistic regression; skeletal maturity.